In [1]:
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
In [2]:
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
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nltk.download('all')

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[nltk_data]    |   Package vader_lexicon is already up-to-date!
[nltk_data]    | Downloading package porter_test to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all
Out[4]:
True
In [5]:
# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_movies = pd.read_csv(path + 'ottmovies.csv')
 
df_movies.head()
Out[5]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type
0 1 Inception 2010 13+ 8.8 87% Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148.0 movie NaN 1 0 0 0 0
1 2 The Matrix 1999 16+ 8.7 88% Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States English Thomas A. Anderson is a man living two lives. ... 136.0 movie NaN 1 0 0 0 0
2 3 Avengers: Infinity War 2018 13+ 8.4 85% Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States English As the Avengers and their allies have continue... 149.0 movie NaN 1 0 0 0 0
3 4 Back to the Future 1985 7+ 8.5 96% Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States English Marty McFly, a typical American teenager of th... 116.0 movie NaN 1 0 0 0 0
4 5 The Good, the Bad and the Ugly 1966 16+ 8.8 97% Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States Italian Blondie (The Good) (Clint Eastwood) is a profe... 161.0 movie NaN 1 0 1 0 0
In [6]:
# profile = ProfileReport(df_movies)
# profile
In [7]:
def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()
In [8]:
data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                 8457
IMDb                 328
Rotten Tomatoes    10437
Directors            357
Cast                 648
Genres               234
Country              303
Language             437
Plotline            4958
Runtime              382
Seasons            16923
dtype: int64
**************************************************
Missing vaules %age wise :

ID                   0.000000
Title                0.000000
Year                 0.000000
Age                 49.973409
IMDb                 1.938191
Rotten Tomatoes     61.673462
Directors            2.109555
Cast                 3.829108
Genres               1.382734
Country              1.790463
Language             2.582284
Plotline            29.297406
Runtime              2.257283
Kind                 0.000000
Seasons            100.000000
Netflix              0.000000
Hulu                 0.000000
Prime Video          0.000000
Disney+              0.000000
Type                 0.000000
dtype: float64
**************************************************
Pictorial Representation :
In [9]:
# ID
# df_movies = df_movies.drop(['ID'], axis = 1)
 
# Age
df_movies.loc[df_movies['Age'].isnull() & df_movies['Disney+'] == 1, "Age"] = '13'
# df_movies.fillna({'Age' : 18}, inplace = True)
df_movies.fillna({'Age' : 'NR'}, inplace = True)
df_movies['Age'].replace({'all': '0'}, inplace = True)
df_movies['Age'].replace({'7+': '7'}, inplace = True)
df_movies['Age'].replace({'13+': '13'}, inplace = True)
df_movies['Age'].replace({'16+': '16'}, inplace = True)
df_movies['Age'].replace({'18+': '18'}, inplace = True)
# df_movies['Age'] = df_movies['Age'].astype(int)
 
# IMDb
# df_movies.fillna({'IMDb' : df_movies['IMDb'].mean()}, inplace = True)
# df_movies.fillna({'IMDb' : df_movies['IMDb'].median()}, inplace = True)
df_movies.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'][df_movies['Rotten Tomatoes'].notnull()].astype(int)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].mean()}, inplace = True)
# df_movies.fillna({'Rotten Tomatoes' : df_movies['Rotten Tomatoes'].median()}, inplace = True)
# df_movies['Rotten Tomatoes'] = df_movies['Rotten Tomatoes'].astype(int)
df_movies.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Casts
# df_movies = df_movies.drop(['Casts'], axis = 1)
df_movies.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_movies.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_movies.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_movies.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_movies.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_movies.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_movies.fillna({'Runtime' : df_movies['Runtime'].mean()}, inplace = True)
# df_movies['Runtime'] = df_movies['Runtime'].astype(int)
df_movies.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_movies.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_movies.fillna({'Type': "NA"}, inplace = True)
# df_movies = df_movies.drop(['Type'], axis = 1)
 
# Seasons
# df_movies.fillna({'Seasons': 1}, inplace = True)
# df_movies.fillna({'Seasons': "NA"}, inplace = True)
df_movies = df_movies.drop(['Seasons'], axis = 1)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
# df_movies.fillna({'Seasons' : df_movies['Seasons'].mean()}, inplace = True)
# df_movies['Seasons'] = df_movies['Seasons'].astype(int)
 
# Service Provider
df_movies['Service Provider'] = df_movies.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_movies.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_movies.dropna(how = 'any', inplace = True)
df_movies.drop_duplicates(inplace = True)
In [10]:
data_investigate(df_movies)

No of Rows :  16923
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation :
In [11]:
df_movies.head()
Out[11]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider
0 1 Inception 2010 13 8.8 87 Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix
1 2 The Matrix 1999 16 8.7 88 Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States English Thomas A. Anderson is a man living two lives. ... 136 movie 1 0 0 0 0 Netflix
2 3 Avengers: Infinity War 2018 13 8.4 85 Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States English As the Avengers and their allies have continue... 149 movie 1 0 0 0 0 Netflix
3 4 Back to the Future 1985 7 8.5 96 Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States English Marty McFly, a typical American teenager of th... 116 movie 1 0 0 0 0 Netflix
4 5 The Good, the Bad and the Ugly 1966 16 8.8 97 Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States Italian Blondie (The Good) (Clint Eastwood) is a profe... 161 movie 1 0 1 0 0 Netflix
In [12]:
df_movies.describe()
Out[12]:
ID Year Netflix Hulu Prime Video Disney+ Type
count 16923.000000 16923.000000 16923.000000 16923.000000 16923.000000 16923.000000 16923.0
mean 8462.000000 2003.211901 0.214915 0.062637 0.727235 0.033150 0.0
std 4885.393638 20.526532 0.410775 0.242315 0.445394 0.179034 0.0
min 1.000000 1901.000000 0.000000 0.000000 0.000000 0.000000 0.0
25% 4231.500000 2001.000000 0.000000 0.000000 0.000000 0.000000 0.0
50% 8462.000000 2012.000000 0.000000 0.000000 1.000000 0.000000 0.0
75% 12692.500000 2016.000000 0.000000 0.000000 1.000000 0.000000 0.0
max 16923.000000 2020.000000 1.000000 1.000000 1.000000 1.000000 0.0
In [13]:
df_movies.corr()
Out[13]:
ID Year Netflix Hulu Prime Video Disney+ Type
ID 1.000000 -0.217816 -0.644470 -0.129926 0.469301 0.263530 NaN
Year -0.217816 1.000000 0.256151 0.101337 -0.255578 -0.047258 NaN
Netflix -0.644470 0.256151 1.000000 -0.118032 -0.745141 -0.089649 NaN
Hulu -0.129926 0.101337 -0.118032 1.000000 -0.284654 -0.039693 NaN
Prime Video 0.469301 -0.255578 -0.745141 -0.284654 1.000000 -0.289008 NaN
Disney+ 0.263530 -0.047258 -0.089649 -0.039693 -0.289008 1.000000 NaN
Type NaN NaN NaN NaN NaN NaN NaN
In [14]:
# df_movies.sort_values('Year', ascending = True)
# df_movies.sort_values('IMDb', ascending = False)
In [15]:
# df_movies.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_ottmovies.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_movies = pd.read_csv(path + 'updated_ottmovies.csv')
 
# udf_movies
In [16]:
# df_netflix_movies = df_movies.loc[(df_movies['Netflix'] > 0)]
# df_hulu_movies = df_movies.loc[(df_movies['Hulu'] > 0)]
# df_prime_video_movies = df_movies.loc[(df_movies['Prime Video'] > 0)]
# df_disney_movies = df_movies.loc[(df_movies['Disney+'] > 0)]
In [17]:
df_netflix_only_movies = df_movies[(df_movies['Netflix'] == 1) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_hulu_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 1) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 0)]
df_prime_video_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 1 ) & (df_movies['Disney+'] == 0)]
df_disney_only_movies = df_movies[(df_movies['Netflix'] == 0) & (df_movies['Hulu'] == 0) & (df_movies['Prime Video'] == 0 ) & (df_movies['Disney+'] == 1)]
In [18]:
df_movies_casts = df_movies.copy()
In [19]:
df_movies_casts.drop(df_movies_casts.loc[df_movies_casts['Cast'] == "NA"].index, inplace = True)
# df_movies_casts = df_movies_casts[df_movies_casts.Cast != "NA"]
# df_movies_casts['Cast'] = df_movies_casts['Cast'].astype(str)
In [20]:
df_movies_count_casts = df_movies_casts.copy()
In [21]:
df_movies_cast = df_movies_casts.copy()
In [22]:
# Create casts dict where key=name and value = number of casts
 
casts = {}
 
for i in df_movies_count_casts['Cast'].dropna():
    if i != "NA":
        #print(i,len(i.split(',')))
        casts[i] = len(i.split(','))
    else:
        casts[i] = 0
    
# Add this information to our dataframe as a new column
 
df_movies_count_casts['Number of Casts'] = df_movies_count_casts['Cast'].map(casts).astype(int)
In [23]:
df_movies_mixed_casts = df_movies_count_casts.copy()
In [24]:
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_casts_movies = df_movies_count_casts.loc[df_movies_count_casts['Netflix'] == 1]
hulu_casts_movies = df_movies_count_casts.loc[df_movies_count_casts['Hulu'] == 1]
prime_video_casts_movies = df_movies_count_casts.loc[df_movies_count_casts['Prime Video'] == 1]
disney_casts_movies = df_movies_count_casts.loc[df_movies_count_casts['Disney+'] == 1]
In [25]:
plt.figure(figsize = (10, 10))
corr = df_movies_count_casts.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>
In [26]:
df_casts_most_movies = df_movies_count_casts.sort_values(by = 'Number of Casts', ascending = False).reset_index()
df_casts_most_movies = df_casts_most_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_casts['Number of Casts'] == (df_movies_count_casts['Number of Casts'].max()))
# df_casts_most_movies = df_movies_count_casts[filter]
 
# mostest_rated_movies = df_movies_count_casts.loc[df_movies_count_casts['Number of Casts'].idxmax()]
 
print('\nMovies with Highest Ever Number of Casts are : \n')
df_casts_most_movies.head(5)

Movies with Highest Ever Number of Casts are :
Out[26]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider Number of Casts
0 380 Heneral Luna 2015 16 7.5 75 Jerrold Tarog John Arcilla,Arron Villaflor,Mon Confiado,Bing... Action,Biography,History,War Philippines ... Set during the Philippine-American war, Henera... 118 movie 1 0 0 0 0 Netflix 1400
1 15970 Around the World in 80 Days 2004 0 6.8 32 Michael Anderson,John Farrow Cantinflas,Finlay Currie,Robert Morley,Ronald ... Adventure,Comedy,Family,Romance United States ... Race car driver, Jim Douglas goes to Monte Car... 167 movie 0 0 0 1 0 Disney+ 1304
2 4624 7 Boxes 2012 16 7.1 97 Juan Carlos Maneglia,Tana Schémbori Celso Franco,Víctor Sosa,Lali Gonzalez,Nico Ga... Adventure,Crime,Drama,Mystery,Thriller Paraguay,Spain ... It's Friday night in Asunción, Paraguay and th... 105 movie 0 0 1 0 0 Prime Video 689
3 4156 Catching Faith 2015 NR 4.8 NA John K.D. Graham Lorena Segura York,Garrett Westton,Bill Engval... Drama,Family,Sport United States ... From the outside, Alexa has the perfect life. ... 88 movie 0 1 1 0 0 Prime Video 491
4 10904 A Broken Code 2012 NR 5.9 NA Josh Webber,Michael Girgenti Michael Girgenti,Josh Webber,Bill T. Williams,... Crime,Drama,Thriller United States ... Each morning, Yusef, a doting father-of-one, h... 90 movie 0 0 1 0 0 Prime Video 476

5 rows × 21 columns

In [27]:
fig = px.bar(y = df_casts_most_movies['Title'][:15],
             x = df_casts_most_movies['Number of Casts'][:15], 
             color = df_casts_most_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Highest Number of Casts : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [28]:
df_casts_least_movies = df_movies_count_casts.sort_values(by = 'Number of Casts', ascending = True).reset_index()
df_casts_least_movies = df_casts_least_movies.drop(['index'], axis = 1)
# filter = (df_movies_count_casts['Number of Casts'] == (df_movies_count_casts['Number of Casts'].min()))
# df_casts_least_movies = df_movies_count_casts[filter]

print('\nMovies with Lowest Ever Number of Casts are : \n')
df_casts_least_movies.head(5)

Movies with Lowest Ever Number of Casts are :
Out[28]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider Number of Casts
0 2004 Ryan Hamilton: Happy Face 2017 NR 7.3 NA Marcus Raboy Ryan Hamilton Documentary,Comedy United States ... NA 56 movie 1 0 0 0 0 Netflix 1
1 2979 Tim Allen: Rewires America 1991 7 5.9 NA Ellen Brown Tim Allen Documentary,Comedy United States ... NA 30 movie 1 0 1 0 0 Netflix 1
2 11983 Dennis Miller: All In 2006 18 5.8 NA James Yukich Dennis Miller Comedy United States ... NA 57 movie 0 0 1 0 0 Prime Video 1
3 8883 Azorian: The Raising of the K-129 2011 7 7.3 NA Michael White Nick Jackson Documentary Austria ... Georgina is an ambitious young London professi... 105 movie 0 0 1 0 0 Prime Video 1
4 1425 Jimmy Carr: Funny Business 2016 NR 7.2 NA Sam Wrench Jimmy Carr Comedy United Kingdom ... NA 62 movie 1 0 0 0 0 Netflix 1

5 rows × 21 columns

In [29]:
fig = px.bar(y = df_casts_least_movies['Title'][:15],
             x = df_casts_least_movies['Number of Casts'][:15], 
             color = df_casts_least_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Lowest Number of Casts : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [30]:
print(f'''
      Total '{df_movies_count_casts['Number of Casts'].unique().shape[0]}' unique Number of Casts s were Given, They were Like this,\n
      
      {df_movies_count_casts.sort_values(by = 'Number of Casts', ascending = False)['Number of Casts'].unique()}\n
 
      The Highest Number of Casts Ever Any Movie Got is '{df_casts_most_movies['Title'][0]}' : '{df_casts_most_movies['Number of Casts'].max()}'\n
 
      The Lowest Number of Casts Ever Any Movie Got is '{df_casts_least_movies['Title'][0]}' : '{df_casts_least_movies['Number of Casts'].min()}'\n
      ''')

      Total '242' unique Number of Casts s were Given, They were Like this,

      
      [1400 1304  689  491  476  472  452  450  449  386  354  325  303  302
  299  295  294  292  281  268  265  260  256  254  253  251  247  245
  241  240  238  237  236  235  233  231  230  227  224  221  217  215
  213  207  206  205  204  203  202  200  199  198  196  195  194  193
  192  191  190  189  188  186  184  183  182  180  179  178  177  175
  174  173  172  171  170  169  168  166  165  164  163  162  161  160
  159  157  156  155  154  153  152  151  150  149  148  147  146  145
  144  143  142  141  140  139  138  137  136  135  134  133  132  131
  130  129  128  127  126  125  124  123  122  121  120  119  118  117
  116  115  114  113  112  111  110  109  108  107  106  105  104  103
  102  101  100   99   98   97   96   95   94   93   92   91   90   89
   88   87   86   85   84   83   82   81   80   79   78   77   76   75
   74   73   72   71   70   69   68   67   66   65   64   63   62   61
   60   59   58   57   56   55   54   53   52   51   50   49   48   47
   46   45   44   43   42   41   40   39   38   37   36   35   34   33
   32   31   30   29   28   27   26   25   24   23   22   21   20   19
   18   17   16   15   14   13   12   11   10    9    8    7    6    5
    4    3    2    1]

 
      The Highest Number of Casts Ever Any Movie Got is 'Heneral Luna' : '1400'

 
      The Lowest Number of Casts Ever Any Movie Got is 'Ryan Hamilton: Happy Face' : '1'
In [31]:
netflix_casts_most_movies = df_casts_most_movies.loc[df_casts_most_movies['Netflix']==1].reset_index()
netflix_casts_most_movies = netflix_casts_most_movies.drop(['index'], axis = 1)
 
netflix_casts_least_movies = df_casts_least_movies.loc[df_casts_least_movies['Netflix']==1].reset_index()
netflix_casts_least_movies = netflix_casts_least_movies.drop(['index'], axis = 1)
 
netflix_casts_most_movies.head(5)
Out[31]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider Number of Casts
0 380 Heneral Luna 2015 16 7.5 75 Jerrold Tarog John Arcilla,Arron Villaflor,Mon Confiado,Bing... Action,Biography,History,War Philippines ... Set during the Philippine-American war, Henera... 118 movie 1 0 0 0 0 Netflix 1400
1 29 The Irishman 2019 16 7.8 62 Martin Scorsese Robert De Niro,Al Pacino,Joe Pesci,Harvey Keit... Biography,Crime,Drama United States ... Frank "The Irishman" Sheeran is a man with a l... 209 movie 1 0 0 0 0 Netflix 472
2 2785 Bring It On: Worldwide #Cheersmack 2017 13 4.3 NA Robert Adetuyi Stephan Benson,Kai Luke Brummer,Sophie de Bruy... Comedy,Romance,Sport United States ... Vivica A. Fox makes her Bring It On debut as C... 95 movie 1 0 0 0 0 Netflix 450
3 1929 Like Arrows 2018 NR 6.5 NA Kevin Peeples Micah Lynn Hanson,Alan Powell,Elizabeth Becka,... Drama United States ... NA 100 movie 1 0 0 0 0 Netflix 303
4 1326 Sandy Wexler 2017 16 5.2 27 Steven Brill Adam Sandler,Jennifer Hudson,Kevin James,Colin... Comedy United States ... Sandy Wexler (Adam Sandler) is a talent manage... 130 movie 1 0 0 0 0 Netflix 260

5 rows × 21 columns

In [32]:
fig = px.bar(y = netflix_casts_most_movies['Title'][:15],
             x = netflix_casts_most_movies['Number of Casts'][:15], 
             color = netflix_casts_most_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Highest Number of Casts : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [33]:
fig = px.bar(y = netflix_casts_least_movies['Title'][:15],
             x = netflix_casts_least_movies['Number of Casts'][:15], 
             color = netflix_casts_least_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Lowest Number of Casts : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [34]:
hulu_casts_most_movies = df_casts_most_movies.loc[df_casts_most_movies['Hulu']==1].reset_index()
hulu_casts_most_movies = hulu_casts_most_movies.drop(['index'], axis = 1)
 
hulu_casts_least_movies = df_casts_least_movies.loc[df_casts_least_movies['Hulu']==1].reset_index()
hulu_casts_least_movies = hulu_casts_least_movies.drop(['index'], axis = 1)
 
hulu_casts_most_movies.head(5)
Out[34]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider Number of Casts
0 4156 Catching Faith 2015 NR 4.8 NA John K.D. Graham Lorena Segura York,Garrett Westton,Bill Engval... Drama,Family,Sport United States ... From the outside, Alexa has the perfect life. ... 88 movie 0 1 1 0 0 Prime Video 491
1 3510 Creed II 2018 13 7.1 83 Steven Caple Jr. Michael B. Jordan,Sylvester Stallone,Tessa Tho... Drama,Sport United States ... Years after Adonis Creed made a name for himse... 130 movie 0 1 1 0 0 Prime Video 299
2 3460 The Dark Knight 2008 13 9 87 Christopher Nolan Christian Bale,Heath Ledger,Aaron Eckhart,Mich... Action,Crime,Drama,Thriller United States,United Kingdom ... Set within a year after the events of Batman B... 152 movie 0 1 0 0 0 Hulu 241
3 3709 Tom of Finland 2017 NR 6.8 84 Dome Karukoski Pekka Strang,Seumas F. Sargent,Chris Myland,Al... Biography,Drama Finland,Sweden,Denmark,Germany,Iceland,United ... ... Touko Laaksonen, a decorated officer, returns ... 117 movie 0 1 0 0 0 Hulu 233
4 3802 C.S.A.: The Confederate States of America 2005 13 6.4 80 Kevin Willmott Greg Kirsch,Renee Patrick,Molly Graham,William... Comedy,Drama,War United States ... Set in an contemporary alternative world where... 89 movie 0 1 0 0 0 Hulu 224

5 rows × 21 columns

In [35]:
fig = px.bar(y = hulu_casts_most_movies['Title'][:15],
             x = hulu_casts_most_movies['Number of Casts'][:15], 
             color = hulu_casts_most_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Highest Number of Casts : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [36]:
fig = px.bar(y = hulu_casts_least_movies['Title'][:15],
             x = hulu_casts_least_movies['Number of Casts'][:15], 
             color = hulu_casts_least_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Lowest Number of Casts : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [37]:
prime_video_casts_most_movies = df_casts_most_movies.loc[df_casts_most_movies['Prime Video']==1].reset_index()
prime_video_casts_most_movies = prime_video_casts_most_movies.drop(['index'], axis = 1)
 
prime_video_casts_least_movies = df_casts_least_movies.loc[df_casts_least_movies['Prime Video']==1].reset_index()
prime_video_casts_least_movies = prime_video_casts_least_movies.drop(['index'], axis = 1)
 
prime_video_casts_most_movies.head(5)
Out[37]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider Number of Casts
0 4624 7 Boxes 2012 16 7.1 97 Juan Carlos Maneglia,Tana Schémbori Celso Franco,Víctor Sosa,Lali Gonzalez,Nico Ga... Adventure,Crime,Drama,Mystery,Thriller Paraguay,Spain ... It's Friday night in Asunción, Paraguay and th... 105 movie 0 0 1 0 0 Prime Video 689
1 4156 Catching Faith 2015 NR 4.8 NA John K.D. Graham Lorena Segura York,Garrett Westton,Bill Engval... Drama,Family,Sport United States ... From the outside, Alexa has the perfect life. ... 88 movie 0 1 1 0 0 Prime Video 491
2 10904 A Broken Code 2012 NR 5.9 NA Josh Webber,Michael Girgenti Michael Girgenti,Josh Webber,Bill T. Williams,... Crime,Drama,Thriller United States ... Each morning, Yusef, a doting father-of-one, h... 90 movie 0 0 1 0 0 Prime Video 476
3 6448 Sharknado 2: The Second One 2014 16 4 61 Anthony C. Ferrante Ian Ziering,Tara Reid,Vivica A. Fox,Mark McGra... Action,Adventure,Comedy,Horror,Sci-Fi,Thriller United States ... It's the closing night at the last drive-in th... 95 movie 0 0 1 0 0 Prime Video 452
4 6592 Angry Video Game Nerd: The Movie 2014 NR 5.6 NA Kevin Finn,James Rolfe James Rolfe,Jeremy Suarez,Sarah Glendening,Bob... Adventure,Comedy,Sci-Fi United States ... Roger Mortis and Doug Bigelow are cops that ar... 115 movie 0 0 1 0 0 Prime Video 449

5 rows × 21 columns

In [38]:
fig = px.bar(y = prime_video_casts_most_movies['Title'][:15],
             x = prime_video_casts_most_movies['Number of Casts'][:15], 
             color = prime_video_casts_most_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Highest Number of Casts : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [39]:
fig = px.bar(y = prime_video_casts_least_movies['Title'][:15],
             x = prime_video_casts_least_movies['Number of Casts'][:15], 
             color = prime_video_casts_least_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Lowest Number of Casts : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [40]:
disney_casts_most_movies = df_casts_most_movies.loc[df_casts_most_movies['Disney+']==1].reset_index()
disney_casts_most_movies = disney_casts_most_movies.drop(['index'], axis = 1)
 
disney_casts_least_movies = df_casts_least_movies.loc[df_casts_least_movies['Disney+']==1].reset_index()
disney_casts_least_movies = disney_casts_least_movies.drop(['index'], axis = 1)
 
disney_casts_most_movies.head(5)
Out[40]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider Number of Casts
0 15970 Around the World in 80 Days 2004 0 6.8 32 Michael Anderson,John Farrow Cantinflas,Finlay Currie,Robert Morley,Ronald ... Adventure,Comedy,Family,Romance United States ... Race car driver, Jim Douglas goes to Monte Car... 167 movie 0 0 0 1 0 Disney+ 1304
1 15875 Muppets Most Wanted 2014 7 6.4 80 James Bobin Ricky Gervais,Ty Burrell,Tina Fey,Steve Whitmi... Adventure,Comedy,Crime,Family,Musical,Mystery United States,United Kingdom ... Three out of work silent movie actors are acci... 107 movie 0 0 0 1 0 Disney+ 302
2 15773 Iron Man 3 2013 13 7.1 79 Shane Black Robert Downey Jr.,Gwyneth Paltrow,Don Cheadle,... Action,Adventure,Sci-Fi United States ... Disney animators set pictures to Western class... 130 movie 0 0 0 1 0 Disney+ 268
3 15750 Captain America: The Winter Soldier 2014 13 7.7 90 Anthony Russo,Joe Russo Chris Evans,Samuel L. Jackson,Scarlett Johanss... Action,Adventure,Sci-Fi,Thriller United States ... Jack Skellington, the pumpkin king of Hallowee... 136 movie 0 0 0 1 0 Disney+ 245
4 15743 Star Wars: Return of the Jedi 1983 7 8.3 82 Richard Marquand Mark Hamill,Harrison Ford,Carrie Fisher,Billy ... Action,Adventure,Fantasy,Sci-Fi United States,United Kingdom ... When a devastating event befalls the city of S... 131 movie 0 0 0 1 0 Disney+ 217

5 rows × 21 columns

In [41]:
fig = px.bar(y = disney_casts_most_movies['Title'][:15],
             x = disney_casts_most_movies['Number of Casts'][:15], 
             color = disney_casts_most_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Highest Number of Casts : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [42]:
fig = px.bar(y = disney_casts_least_movies['Title'][:15],
             x = disney_casts_least_movies['Number of Casts'][:15], 
             color = disney_casts_least_movies['Number of Casts'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Casts'},
             title  = 'Movies with Lowest Number of Casts : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [43]:
print(f'''
      The Movie with Highest Number of Casts Ever Got is '{df_casts_most_movies['Title'][0]}' : '{df_casts_most_movies['Number of Casts'].max()}'\n
      The Movie with Lowest Number of Casts Ever Got is '{df_casts_least_movies['Title'][0]}' : '{df_casts_least_movies['Number of Casts'].min()}'\n
      
      The Movie with Highest Number of Casts on 'Netflix' is '{netflix_casts_most_movies['Title'][0]}' : '{netflix_casts_most_movies['Number of Casts'].max()}'\n
      The Movie with Lowest Number of Casts on 'Netflix' is '{netflix_casts_least_movies['Title'][0]}' : '{netflix_casts_least_movies['Number of Casts'].min()}'\n
      
      The Movie with Highest Number of Casts on 'Hulu' is '{hulu_casts_most_movies['Title'][0]}' : '{hulu_casts_most_movies['Number of Casts'].max()}'\n
      The Movie with Lowest Number of Casts on 'Hulu' is '{hulu_casts_least_movies['Title'][0]}' : '{hulu_casts_least_movies['Number of Casts'].min()}'\n
      
      The Movie with Highest Number of Casts on 'Prime Video' is '{prime_video_casts_most_movies['Title'][0]}' : '{prime_video_casts_most_movies['Number of Casts'].max()}'\n
      The Movie with Lowest Number of Casts on 'Prime Video' is '{prime_video_casts_least_movies['Title'][0]}' : '{prime_video_casts_least_movies['Number of Casts'].min()}'\n
      
      The Movie with Highest Number of Casts on 'Disney+' is '{disney_casts_most_movies['Title'][0]}' : '{disney_casts_most_movies['Number of Casts'].max()}'\n
      The Movie with Lowest Number of Casts on 'Disney+' is '{disney_casts_least_movies['Title'][0]}' : '{disney_casts_least_movies['Number of Casts'].min()}'\n 
      ''')

      The Movie with Highest Number of Casts Ever Got is 'Heneral Luna' : '1400'

      The Movie with Lowest Number of Casts Ever Got is 'Ryan Hamilton: Happy Face' : '1'

      
      The Movie with Highest Number of Casts on 'Netflix' is 'Heneral Luna' : '1400'

      The Movie with Lowest Number of Casts on 'Netflix' is 'Ryan Hamilton: Happy Face' : '1'

      
      The Movie with Highest Number of Casts on 'Hulu' is 'Catching Faith' : '491'

      The Movie with Lowest Number of Casts on 'Hulu' is 'Alien Contact' : '1'

      
      The Movie with Highest Number of Casts on 'Prime Video' is '7 Boxes' : '689'

      The Movie with Lowest Number of Casts on 'Prime Video' is 'Tim Allen: Rewires America' : '1'

      
      The Movie with Highest Number of Casts on 'Disney+' is 'Around the World in 80 Days' : '1304'

      The Movie with Lowest Number of Casts on 'Disney+' is 'The African Lion' : '1'
In [44]:
print(f'''
      Accross All Platforms the Average Number of Casts is '{round(df_movies_count_casts['Number of Casts'].mean(), ndigits = 2)}'\n
      The Average Number of Casts on 'Netflix' is '{round(netflix_casts_movies['Number of Casts'].mean(), ndigits = 2)}'\n
      The Average Number of Casts on 'Hulu' is '{round(hulu_casts_movies['Number of Casts'].mean(), ndigits = 2)}'\n
      The Average Number of Casts on 'Prime Video' is '{round(prime_video_casts_movies['Number of Casts'].mean(), ndigits = 2)}'\n
      The Average Number of Casts on 'Disney+' is '{round(disney_casts_movies['Number of Casts'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Number of Casts is '32.64'

      The Average Number of Casts on 'Netflix' is '34.47'

      The Average Number of Casts on 'Hulu' is '41.65'

      The Average Number of Casts on 'Prime Video' is '31.11'

      The Average Number of Casts on 'Disney+' is '49.06'
In [45]:
print(f'''
      Accross All Platforms Total Count of Cast is '{df_movies_count_casts['Number of Casts'].max()}'\n
      Total Count of Cast on 'Netflix' is '{netflix_casts_movies['Number of Casts'].max()}'\n
      Total Count of Cast on 'Hulu' is '{hulu_casts_movies['Number of Casts'].max()}'\n
      Total Count of Cast on 'Prime Video' is '{prime_video_casts_movies['Number of Casts'].max()}'\n
      Total Count of Cast on 'Disney+' is '{disney_casts_movies['Number of Casts'].max()}'\n 
      ''')

      Accross All Platforms Total Count of Cast is '1400'

      Total Count of Cast on 'Netflix' is '1400'

      Total Count of Cast on 'Hulu' is '491'

      Total Count of Cast on 'Prime Video' is '689'

      Total Count of Cast on 'Disney+' is '1304'
In [46]:
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_movies_count_casts['Number of Casts'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_movies_count_casts['Number of Casts'], ax = ax[1])
plt.show()
In [47]:
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Casts s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_casts_movies['Number of Casts'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_casts_movies['Number of Casts'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_casts_movies['Number of Casts'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_casts_movies['Number of Casts'], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
In [48]:
df_lan = df_movies_cast['Cast'].str.split(',').apply(pd.Series).stack()
del df_movies_cast['Cast']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Cast'
df_movies_cast = df_movies_cast.join(df_lan)
df_movies_cast.drop_duplicates(inplace = True)
In [49]:
df_movies_cast.head(5)
Out[49]:
ID Title Year Age IMDb Rotten Tomatoes Directors Genres Country Language Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider Cast
0 1 Inception 2010 13 8.8 87 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix Leonardo DiCaprio
0 1 Inception 2010 13 8.8 87 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix Joseph Gordon-Levitt
0 1 Inception 2010 13 8.8 87 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix Elliot Page
0 1 Inception 2010 13 8.8 87 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix Tom Hardy
0 1 Inception 2010 13 8.8 87 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix Ken Watanabe
In [50]:
cast_count = df_movies_cast.groupby('Cast')['Title'].count()
cast_movies = df_movies_cast.groupby('Cast')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
cast_data_movies = pd.concat([cast_count, cast_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count'})
cast_data_movies = cast_data_movies.sort_values(by = 'Movies Count', ascending = False)
In [51]:
# Cast with Movies Counts - All Platforms Combined
cast_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]
Out[51]:
Cast Movies Count Netflix Hulu Prime Video Disney+
99089 Frank Welker 84 16 14 16 42
116679 Herman Hack 74 0 0 72 2
13322 Amitabh Bachchan 67 24 0 53 0
256998 Ronald Reagan 66 19 8 40 0
264023 Samuel L. Jackson 63 15 6 32 18
22172 Anupam Kher 61 27 2 36 0
3093 Adolf Hitler 61 14 5 45 3
140977 Jim Cummings 60 11 7 8 35
91411 Eric Roberts 58 2 2 54 0
28049 Barack Obama 57 23 6 27 2
In [52]:
fig = px.bar(x = cast_data_movies['Cast'][:50],
             y = cast_data_movies['Movies Count'][:50], 
             color = cast_data_movies['Movies Count'][:50],
             color_continuous_scale = 'Teal_r', 
             labels = { 'x' : 'Cast', 'y' : 'Movies Count'},
             title  = 'Major Casts : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [53]:
df_cast_high_movies = cast_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_cast_high_movies = df_cast_high_movies.drop(['index'], axis = 1)
# filter = (cast_data_movies['Movies Count'] == (cast_data_movies['Movies Count'].max()))
# df_cast_high_movies = cast_data_movies[filter]
 
# highest_rated_movies = cast_data_movies.loc[cast_data_movies['Movies Count'].idxmax()]
 
print('\nCast with Highest Ever Movies Count are : All Platforms Combined\n')
df_cast_high_movies.head(5)

Cast with Highest Ever Movies Count are : All Platforms Combined
Out[53]:
Cast Movies Count Netflix Hulu Prime Video Disney+
0 Frank Welker 84 16 14 16 42
1 Herman Hack 74 0 0 72 2
2 Amitabh Bachchan 67 24 0 53 0
3 Ronald Reagan 66 19 8 40 0
4 Samuel L. Jackson 63 15 6 32 18
In [54]:
fig = px.bar(y = df_cast_high_movies['Cast'][:15],
             x = df_cast_high_movies['Movies Count'][:15], 
             color = df_cast_high_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Cast', 'x' : 'Movies Count'},
             title  = 'Cast with Highest Movies : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [55]:
df_cast_low_movies = cast_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_cast_low_movies = df_cast_low_movies.drop(['index'], axis = 1)
# filter = (cast_data_movies['Movies Count'] == (cast_data_movies['Movies Count'].min()))
# df_cast_low_movies = cast_data_movies[filter]

print('\nCast with Lowest Ever Movies Count are : All Platforms Combined\n')
df_cast_low_movies.head(5)

Cast with Lowest Ever Movies Count are : All Platforms Combined
Out[55]:
Cast Movies Count Netflix Hulu Prime Video Disney+
0 Oliver Lewis 1 0 0 1 0
1 Dave Parks 1 0 0 1 0
2 Dave Paquitte 1 0 0 1 0
3 Dave Padayao 1 1 0 0 0
4 Dave Osterman 1 0 0 1 0
In [56]:
fig = px.bar(y = df_cast_low_movies['Cast'][:15],
             x = df_cast_low_movies['Movies Count'][:15], 
             color = df_cast_low_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Cast', 'x' : 'Movies Count'},
             title  = 'Cast with Lowest Movies Count : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [57]:
print(f'''
      Total '{cast_data_movies['Cast'].unique().shape[0]}' unique Cast Count s were Given, They were Like this,\n
      
      {cast_data_movies.sort_values(by = 'Movies Count', ascending = False)['Cast'].unique()[:5]}\n
 
      The Highest Ever Movies Count Ever Any Movie Got is '{df_cast_high_movies['Cast'][0]}' : '{df_cast_high_movies['Movies Count'].max()}'\n
 
      The Lowest Ever Movies Count Ever Any Movie Got is '{df_cast_low_movies['Cast'][0]}' : '{df_cast_low_movies['Movies Count'].min()}'\n
      ''')

      Total '315871' unique Cast Count s were Given, They were Like this,

      
      ['Frank Welker' 'Herman Hack' 'Amitabh Bachchan' 'Ronald Reagan'
 'Samuel L. Jackson']

 
      The Highest Ever Movies Count Ever Any Movie Got is 'Frank Welker' : '84'

 
      The Lowest Ever Movies Count Ever Any Movie Got is 'Oliver Lewis' : '1'
In [58]:
fig = px.pie(cast_data_movies[:10], names = 'Cast', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Cast')
fig.show()
In [59]:
# netflix_cast_movies = cast_data_movies[cast_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_cast_movies = netflix_cast_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
netflix_cast_high_movies = df_cast_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_cast_high_movies = netflix_cast_high_movies.drop(['index'], axis = 1)
 
netflix_cast_low_movies = df_cast_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_cast_low_movies = netflix_cast_low_movies.drop(['index'], axis = 1)
 
netflix_cast_high_movies.head(5)
Out[59]:
Cast Movies Count Netflix Hulu Prime Video Disney+
0 Shah Rukh Khan 49 30 0 21 0
1 Anupam Kher 61 27 2 36 0
2 Amitabh Bachchan 67 24 0 53 0
3 Fred Tatasciore 45 24 5 8 11
4 Paresh Rawal 43 23 1 25 0
In [60]:
fig = px.bar(x = netflix_cast_high_movies['Cast'][:15],
             y = netflix_cast_high_movies['Netflix'][:15], 
             color = netflix_cast_high_movies['Netflix'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Cast', 'x' : 'Movies Count'},
             title  = 'Cast with Highest Movies : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [61]:
# hulu_cast_movies = cast_data_movies[cast_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_cast_movies = hulu_cast_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
hulu_cast_high_movies = df_cast_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_cast_high_movies = hulu_cast_high_movies.drop(['index'], axis = 1)
 
hulu_cast_low_movies = df_cast_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_cast_low_movies = hulu_cast_low_movies.drop(['index'], axis = 1)
 
hulu_cast_high_movies.head(5)
Out[61]:
Cast Movies Count Netflix Hulu Prime Video Disney+
0 Frank Welker 84 16 14 16 42
1 Nicolas Cage 37 11 9 18 2
2 Liam Neeson 27 6 9 11 6
3 Bill Nighy 24 6 9 12 3
4 Todd Haberkorn 23 11 8 6 0
In [62]:
fig = px.bar(x = hulu_cast_high_movies['Cast'][:15],
             y = hulu_cast_high_movies['Hulu'][:15], 
             color = hulu_cast_high_movies['Hulu'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Cast', 'x' : 'Movies Count'},
             title  = 'Cast with Highest Movies : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [63]:
# prime_video_cast_movies = cast_data_movies[cast_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_cast_movies = prime_video_cast_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
 
prime_video_cast_high_movies = df_cast_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_cast_high_movies = prime_video_cast_high_movies.drop(['index'], axis = 1)
 
prime_video_cast_low_movies = df_cast_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_cast_low_movies = prime_video_cast_low_movies.drop(['index'], axis = 1)
 
prime_video_cast_high_movies.head(5)
Out[63]:
Cast Movies Count Netflix Hulu Prime Video Disney+
0 Herman Hack 74 0 0 72 2
1 George Morrell 56 0 0 56 0
2 Eric Roberts 58 2 2 54 0
3 Amitabh Bachchan 67 24 0 53 0
4 George 'Gabby' Hayes 47 0 0 47 0
In [64]:
fig = px.bar(x = prime_video_cast_high_movies['Cast'][:15],
             y = prime_video_cast_high_movies['Prime Video'][:15], 
             color = prime_video_cast_high_movies['Prime Video'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Cast', 'x' : 'Movies Count'},
             title  = 'Cast with Highest Movies : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [65]:
# disney_cast_movies = cast_data_movies[cast_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_cast_movies = disney_cast_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
 
disney_cast_high_movies = df_cast_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_cast_high_movies = disney_cast_high_movies.drop(['index'], axis = 1)
 
disney_cast_low_movies = df_cast_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_cast_low_movies = disney_cast_low_movies.drop(['index'], axis = 1)
 
disney_cast_high_movies.head(5)
Out[65]:
Cast Movies Count Netflix Hulu Prime Video Disney+
0 Frank Welker 84 16 14 16 42
1 Jeff Bennett 53 8 6 4 36
2 Jim Cummings 60 11 7 8 35
3 Kath Soucie 38 4 4 6 26
4 Stan Lee 44 5 2 16 26
In [66]:
fig = px.bar(x = disney_cast_high_movies['Cast'][:15],
             y = disney_cast_high_movies['Disney+'][:15], 
             color = disney_cast_high_movies['Disney+'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Cast', 'x' : 'Movies Count'},
             title  = 'Cast with Highest Movies : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [67]:
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(cast_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(cast_data_movies['Movies Count'], ax = ax[1])
plt.show()
In [68]:
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_cast_movies = cast_data_movies[cast_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_cast_movies = netflix_cast_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

hulu_cast_movies = cast_data_movies[cast_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_cast_movies = hulu_cast_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

prime_video_cast_movies = cast_data_movies[cast_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_cast_movies = prime_video_cast_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)

disney_cast_movies = cast_data_movies[cast_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_cast_movies = disney_cast_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
In [69]:
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Cast Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(disney_cast_movies['Disney+'][:50], color = 'darkblue', legend = True, kde = True)  
sns.histplot(prime_video_cast_movies['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_cast_movies['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_cast_movies['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()
In [70]:
print(f'''
      The Cast with Highest Movies Count Ever Got is '{df_cast_high_movies['Cast'][0]}' : '{df_cast_high_movies['Movies Count'].max()}'\n
      The Cast with Lowest Movies Count Ever Got is '{df_cast_low_movies['Cast'][0]}' : '{df_cast_low_movies['Movies Count'].min()}'\n
      
      The Cast with Highest Movies Count on 'Netflix' is '{netflix_cast_high_movies['Cast'][0]}' : '{netflix_cast_high_movies['Netflix'].max()}'\n
      The Cast with Lowest Movies Count on 'Netflix' is '{netflix_cast_low_movies['Cast'][0]}' : '{netflix_cast_low_movies['Netflix'].min()}'\n
      
      The Cast with Highest Movies Count on 'Hulu' is '{hulu_cast_high_movies['Cast'][0]}' : '{hulu_cast_high_movies['Hulu'].max()}'\n
      The Cast with Lowest Movies Count on 'Hulu' is '{hulu_cast_low_movies['Cast'][0]}' : '{hulu_cast_low_movies['Hulu'].min()}'\n
      
      The Cast with Highest Movies Count on 'Prime Video' is '{prime_video_cast_high_movies['Cast'][0]}' : '{prime_video_cast_high_movies['Prime Video'].max()}'\n
      The Cast with Lowest Movies Count on 'Prime Video' is '{prime_video_cast_low_movies['Cast'][0]}' : '{prime_video_cast_low_movies['Prime Video'].min()}'\n
      
      The Cast with Highest Movies Count on 'Disney+' is '{disney_cast_high_movies['Cast'][0]}' : '{disney_cast_high_movies['Disney+'].max()}'\n
      The Cast with Lowest Movies Count on 'Disney+' is '{disney_cast_low_movies['Cast'][0]}' : '{disney_cast_low_movies['Disney+'].min()}'\n 
      ''')

      The Cast with Highest Movies Count Ever Got is 'Frank Welker' : '84'

      The Cast with Lowest Movies Count Ever Got is 'Oliver Lewis' : '1'

      
      The Cast with Highest Movies Count on 'Netflix' is 'Shah Rukh Khan' : '30'

      The Cast with Lowest Movies Count on 'Netflix' is 'Hayley Norris' : '0'

      
      The Cast with Highest Movies Count on 'Hulu' is 'Frank Welker' : '14'

      The Cast with Lowest Movies Count on 'Hulu' is 'Hayley Norris' : '0'

      
      The Cast with Highest Movies Count on 'Prime Video' is 'Herman Hack' : '72'

      The Cast with Lowest Movies Count on 'Prime Video' is 'Rob Moore' : '0'

      
      The Cast with Highest Movies Count on 'Disney+' is 'Frank Welker' : '42'

      The Cast with Lowest Movies Count on 'Disney+' is 'Hayley Norris' : '0'
In [71]:
# Distribution of movies cast in each platform
plt.figure(figsize = (20, 5))
plt.title('Cast with Movies Count for All Platforms')
sns.violinplot(x = cast_data_movies['Movies Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()
In [72]:
# Distribution of Cast Movies Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_cast_movies['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_cast_movies['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
 
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_cast_movies['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_cast_movies['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()
In [73]:
print(f'''
      Accross All Platforms the Average Movies Count of Cast is '{round(cast_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Cast on 'Netflix' is '{round(netflix_cast_movies['Netflix'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Cast on 'Hulu' is '{round(hulu_cast_movies['Hulu'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Cast on 'Prime Video' is '{round(prime_video_cast_movies['Prime Video'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Cast on 'Disney+' is '{round(disney_cast_movies['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Movies Count of Cast is '1.68'

      The Average Movies Count of Cast on 'Netflix' is '1.32'

      The Average Movies Count of Cast on 'Hulu' is '1.15'

      The Average Movies Count of Cast on 'Prime Video' is '1.52'

      The Average Movies Count of Cast on 'Disney+' is '1.31'
In [74]:
print(f'''
      Accross All Platforms Total Count of Cast is '{cast_data_movies['Cast'].unique().shape[0]}'\n
      Total Count of Cast on 'Netflix' is '{netflix_cast_movies['Cast'].unique().shape[0]}'\n
      Total Count of Cast on 'Hulu' is '{hulu_cast_movies['Cast'].unique().shape[0]}'\n
      Total Count of Cast on 'Prime Video' is '{prime_video_cast_movies['Cast'].unique().shape[0]}'\n
      Total Count of Cast on 'Disney+' is '{disney_cast_movies['Cast'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Cast is '315871'

      Total Count of Cast on 'Netflix' is '91466'

      Total Count of Cast on 'Hulu' is '37628'

      Total Count of Cast on 'Prime Video' is '240844'

      Total Count of Cast on 'Disney+' is '20717'
In [75]:
plt.figure(figsize = (20, 5))
sns.lineplot(x = cast_data_movies['Cast'][:10], y = cast_data_movies['Netflix'][:10], color = 'red')
sns.lineplot(x = cast_data_movies['Cast'][:10], y = cast_data_movies['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = cast_data_movies['Cast'][:10], y = cast_data_movies['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = cast_data_movies['Cast'][:10], y = cast_data_movies['Disney+'][:10], color = 'darkblue')
plt.xlabel('Cast', fontsize = 20)
plt.ylabel('Movies Count', fontsize = 20)
plt.show()
In [76]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_c_ax1 = sns.lineplot(y = cast_data_movies['Cast'][:10], x = cast_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_c_ax2 = sns.lineplot(y = cast_data_movies['Cast'][:10], x = cast_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_c_ax3 = sns.lineplot(y = cast_data_movies['Cast'][:10], x = cast_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_c_ax4 = sns.lineplot(y = cast_data_movies['Cast'][:10], x = cast_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_c_ax1.title.set_text(labels[0])
h_c_ax2.title.set_text(labels[1])
p_c_ax3.title.set_text(labels[2])
d_c_ax4.title.set_text(labels[3])
 
plt.show()
In [77]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_c_ax1 = sns.barplot(y = netflix_cast_movies['Cast'][:10], x = netflix_cast_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_c_ax2 = sns.barplot(y = hulu_cast_movies['Cast'][:10], x = hulu_cast_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_c_ax3 = sns.barplot(y = prime_video_cast_movies['Cast'][:10], x = prime_video_cast_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_c_ax4 = sns.barplot(y = disney_cast_movies['Cast'][:10], x = disney_cast_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_c_ax1.title.set_text(labels[0])
h_c_ax2.title.set_text(labels[1])
p_c_ax3.title.set_text(labels[2])
d_c_ax4.title.set_text(labels[3])
 
plt.show()
In [78]:
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Cast  Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_cast_movies['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_cast_movies['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_cast_movies['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_cast_movies['Disney+'][:10], color = 'darkblue', legend = True)                                      
                                      
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
In [79]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_c_ax1 = sns.barplot(y = cast_data_movies['Cast'][:10], x = cast_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_c_ax2 = sns.barplot(y = cast_data_movies['Cast'][:10], x = cast_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_c_ax3 = sns.barplot(y = cast_data_movies['Cast'][:10], x = cast_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_c_ax4 = sns.barplot(y = cast_data_movies['Cast'][:10], x = cast_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_c_ax1.title.set_text(labels[0])
h_c_ax2.title.set_text(labels[1])
p_c_ax3.title.set_text(labels[2])
d_c_ax4.title.set_text(labels[3])
 
plt.show()
In [80]:
df_movies_mixed_casts.drop(df_movies_mixed_casts.loc[df_movies_mixed_casts['Cast'] == "NA"].index, inplace = True)
# df_movies_mixed_casts = df_movies_mixed_casts[df_movies_mixed_casts.Cast != "NA"]
df_movies_mixed_casts.drop(df_movies_mixed_casts.loc[df_movies_mixed_casts['Number of Casts'] == 1].index, inplace = True)
In [81]:
df_movies_mixed_casts.head(5)
Out[81]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Netflix Hulu Prime Video Disney+ Type Service Provider Number of Casts
0 1 Inception 2010 13 8.8 87 Christopher Nolan Leonardo DiCaprio,Joseph Gordon-Levitt,Elliot ... Action,Adventure,Sci-Fi,Thriller United States,United Kingdom ... Dom Cobb is a skilled thief, the absolute best... 148 movie 1 0 0 0 0 Netflix 84
1 2 The Matrix 1999 16 8.7 88 Lana Wachowski,Lilly Wachowski Keanu Reeves,Laurence Fishburne,Carrie-Anne Mo... Action,Sci-Fi United States ... Thomas A. Anderson is a man living two lives. ... 136 movie 1 0 0 0 0 Netflix 41
2 3 Avengers: Infinity War 2018 13 8.4 85 Anthony Russo,Joe Russo Robert Downey Jr.,Chris Hemsworth,Mark Ruffalo... Action,Adventure,Sci-Fi United States ... As the Avengers and their allies have continue... 149 movie 1 0 0 0 0 Netflix 141
3 4 Back to the Future 1985 7 8.5 96 Robert Zemeckis Michael J. Fox,Christopher Lloyd,Lea Thompson,... Adventure,Comedy,Sci-Fi United States ... Marty McFly, a typical American teenager of th... 116 movie 1 0 0 0 0 Netflix 59
4 5 The Good, the Bad and the Ugly 1966 16 8.8 97 Sergio Leone Eli Wallach,Clint Eastwood,Lee Van Cleef,Aldo ... Western Italy,Spain,West Germany,United States ... Blondie (The Good) (Clint Eastwood) is a profe... 161 movie 1 0 1 0 0 Netflix 54

5 rows × 21 columns

In [82]:
mixed_casts_count = df_movies_mixed_casts.groupby('Cast')['Title'].count()
mixed_casts_movies = df_movies_mixed_casts.groupby('Cast')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_casts_data_movies = pd.concat([mixed_casts_count, mixed_casts_movies], axis = 1).reset_index().rename(columns = {'Title' : 'Movies Count', 'Cast' : 'Mixed Cast'})
mixed_casts_data_movies = mixed_casts_data_movies.sort_values(by = 'Movies Count', ascending = False)
In [83]:
mixed_casts_data_movies.head(5)
Out[83]:
Mixed Cast Movies Count Netflix Hulu Prime Video Disney+
3678 Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... 5 2 0 4 0
13311 Shauna Macdonald,Natalie Mendoza,Alex Reid,Sas... 4 0 1 4 0
4304 Elliot Page,Hayley McFarland,Nick Searcy,Romy ... 3 1 0 2 0
7261 John Friedrich,Adrian Zmed,Ernest Harden Jr.,L... 3 0 0 3 0
3888 Dominique Swain,Brad Renfro,Bijou Phillips,Mis... 3 0 0 3 0
In [84]:
# Mixed Cast with Movies Counts - All Platforms Combined
mixed_casts_data_movies.sort_values(by = 'Movies Count', ascending = False)[:10]
Out[84]:
Mixed Cast Movies Count Netflix Hulu Prime Video Disney+
3678 Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... 5 2 0 4 0
13311 Shauna Macdonald,Natalie Mendoza,Alex Reid,Sas... 4 0 1 4 0
4304 Elliot Page,Hayley McFarland,Nick Searcy,Romy ... 3 1 0 2 0
7261 John Friedrich,Adrian Zmed,Ernest Harden Jr.,L... 3 0 0 3 0
3888 Dominique Swain,Brad Renfro,Bijou Phillips,Mis... 3 0 0 3 0
15240 Yuliya Peresild,Evgeniy Tsyganov,Oleg Vasilkov... 3 0 0 3 0
3608 Deepak Chachra,Smita Malhotra,Tarun Ratnani 3 3 0 0 0
10000 Mel Brooks,Dom DeLuise,Madeline Kahn,Harvey Ko... 3 0 0 3 0
1383 Barbara Alyn Woods,Tina Ivlev,Lexi Ainsworth,B... 2 0 0 2 0
13732 Steve Carell,Sheryl Lee,Todd Weeks,Paul Schack... 2 0 0 2 0
In [85]:
df_mixed_casts_high_movies = mixed_casts_data_movies.sort_values(by = 'Movies Count', ascending = False).reset_index()
df_mixed_casts_high_movies = df_mixed_casts_high_movies.drop(['index'], axis = 1)
# filter = (mixed_casts_data_movies['Movies Count'] = =  (mixed_casts_data_movies['Movies Count'].max()))
# df_mixed_casts_high_movies = mixed_casts_data_movies[filter]
 
# highest_rated_movies = mixed_casts_data_movies.loc[mixed_casts_data_movies['Movies Count'].idxmax()]
 
print('\nMixed Cast with Highest Ever Movies Count are : All Platforms Combined\n')
df_mixed_casts_high_movies.head(5)

Mixed Cast with Highest Ever Movies Count are : All Platforms Combined
Out[85]:
Mixed Cast Movies Count Netflix Hulu Prime Video Disney+
0 Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... 5 2 0 4 0
1 Shauna Macdonald,Natalie Mendoza,Alex Reid,Sas... 4 0 1 4 0
2 Elliot Page,Hayley McFarland,Nick Searcy,Romy ... 3 1 0 2 0
3 John Friedrich,Adrian Zmed,Ernest Harden Jr.,L... 3 0 0 3 0
4 Dominique Swain,Brad Renfro,Bijou Phillips,Mis... 3 0 0 3 0
In [86]:
fig = px.bar(y = df_mixed_casts_high_movies['Mixed Cast'][:15],
             x = df_mixed_casts_high_movies['Movies Count'][:15], 
             color = df_mixed_casts_high_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Cast'},
             title  = 'Movies with Highest Number of Mixed Casts : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [87]:
df_mixed_casts_low_movies = mixed_casts_data_movies.sort_values(by = 'Movies Count', ascending = True).reset_index()
df_mixed_casts_low_movies = df_mixed_casts_low_movies.drop(['index'], axis = 1)
# filter = (mixed_casts_data_movies['Movies Count'] = =  (mixed_casts_data_movies['Movies Count'].min()))
# df_mixed_casts_low_movies = mixed_casts_data_movies[filter]
 
print('\nMixed Cast with Lowest Ever Movies Count are : All Platforms Combined\n')
df_mixed_casts_low_movies.head(5)

Mixed Cast with Lowest Ever Movies Count are : All Platforms Combined
Out[87]:
Mixed Cast Movies Count Netflix Hulu Prime Video Disney+
0 Ryan Barton-Grimley,Jeremy Clark,Jes Mercer,Jo... 1 0 0 1 0
1 Adriano Tardiolo,Agnese Graziani,Alba Rohrwach... 1 1 0 0 0
2 Adrien Brody,Antonio Banderas,John Malkovich,R... 1 1 0 0 0
3 Adrien Brody,Emilia Fox,Michal Zebrowski,Ed St... 1 1 0 1 0
4 Adrien Gamache,Nathan Hamer,Trey Hatch,Selena ... 1 0 0 1 0
In [88]:
fig = px.bar(y = df_mixed_casts_low_movies['Mixed Cast'][:15],
             x = df_mixed_casts_low_movies['Movies Count'][:15], 
             color = df_mixed_casts_low_movies['Movies Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Movies', 'x' : 'Number of Mixed Cast'},
             title  = 'Movies with Lowest Number of Mixed Casts : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()
In [89]:
print(f'''
      Total '{df_movies_casts['Cast'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see Movies from Total '{mixed_casts_data_movies['Mixed Cast'].unique().shape[0]}' Mixed Cast, They were Like this, \n
 
      {mixed_casts_data_movies.sort_values(by = 'Movies Count', ascending = False)['Mixed Cast'].head(5).unique()} etc. \n
 
      The Mixed Cast with Highest Movies Count have '{mixed_casts_data_movies['Movies Count'].max()}' Movies Available is '{df_mixed_casts_high_movies['Mixed Cast'][0]}', &\n
      The Mixed Cast with Lowest Movies Count have '{mixed_casts_data_movies['Movies Count'].min()}' Movies Available is '{df_mixed_casts_low_movies['Mixed Cast'][0]}'
      ''')

      Total '16275' Titles are available on All Platforms, out of which

      You Can Choose to see Movies from Total '15354' Mixed Cast, They were Like this, 

 
      ['Dennis Quaid,Greg Kinnear,Common,Charlie Saxton,Will Sasso,Odessa Rae,Seth MacFarlane,Mike Meldman,Hugh Jackman,Kate Winslet,Julie Claire,Katie Finneran,Roy Jenkins,Rocky Russo,Anna Madigan,Jeremy Allen White,Liev Schreiber,Naomi Watts,Alex Cranmer,Julie Ann Emery,Austin Cope,Anna Faris,Chris Pratt,J.B. Smoove,Jarrad Paul,Maria Arcé,Aaron LaPlante,Kieran Culkin,Emma Stone,Arthur French,Josh Shuman,Cathy Cliften,Cherina Monteniques Scott,Richard Gere,Kate Bosworth,Jack McBrayer,Aasif Mandvi,Zach Lasry,Darby Lynn Totten,Marc Ambrose,Justin Long,Jason Sudeikis,Uma Thurman,Bobby Cannavale,Kristen Bell,John Hodgman,Leslie Bibb,Will Carlough,Katrina Bowden,Phil Crowley,Ed Blythe,Olivia Roberts Payne,Monique Delee,Julie McNiven,Jaron Henrie-McCrea,Sebastian Banes,Severyn Banes,Rightor Doyle,Raven Burnett,Montana Byrne,Jarrett Carter,Phillip Michaels,Christopher Mintz-Plasse,Chloë Grace Moretz,Patrick Warburton,Jimmy Bennett,Matt Walsh,Coleen Hoffman,Elbe Van Der Merwe,Gerard Butler,Seann William Scott,Johnny Knoxville,Esti Ginzburg,Halle Berry,Stephen Merchant,Sayed Badreya,Nicole Polizzi,Caryl West,Ricki Lander,Paloma Felisberto Bilson,Jasper Grey,Benny Harris,Zen Gesner,Terrence Howard,Aaron Jennings,Jared Dudley,Corey Brewer,Larry Eugene Sanders II,Jay Ellis,Brian Flaccus,Brett Davern,Evan Dumouchel,Sean A. Rosales,Logan Holladay,Mandy Kowalski,Eric Stuart,Elizabeth Banks,Josh Duhamel,Emily Alyn Lind,Michelle Gunn,Christina Linhardt,Greg Oliver Bodine,Adam Cagley,Liz Carey,Bronwyn Cornelius,Devin Eash,Ted Ely,Peter Farrelly,Jackie Flynn,Nate Hartley,James Hsu,Sara Kessler,Christopher Kirby,Martin Klebba,Kurt Leitner,Beth Littleford,Michael Marrio,Rob Moran,Eddie Napolillo,Vincent Ortega,George Paez,Victor Pagan,Martin Pfefferkorn,Tony Shalhoub,Davi Shane,Nicole Joy Tan,Charles B. Wessler,Mark L. Young'
 'Shauna Macdonald,Natalie Mendoza,Alex Reid,Saskia Mulder,MyAnna Buring,Nora-Jane Noone,Oliver Milburn,Molly Kayll,Craig Conway,Leslie Simpson,Mark Cronfield,Stephen Lamb,Catherine Dyson,Julie Ellis,Sophie Trott,Tristan Matthiae,Stuart Luis,Justin Hackney'
 "Elliot Page,Hayley McFarland,Nick Searcy,Romy Rosemont,Catherine Keener,Ari Graynor,Scout Taylor-Compton,Tristan Jarred,Hannah Leigh,Bradley Whitford,Michael O'Keefe,Carlie Westerman,Michelle Benes,Patricia Place,Calvin Keet,Jeremy Sumpter,James Franco,Brian Geraghty,Channing Nichols,Eliza Dean,Michael Welch,Evan Peters,Amy Ferguson,Scott Eastwood,Will Carter,Edward James Gage,Kuma,Chris Harris,James Meehan,Paul Palo,Isabel Shaw,Gary Sievers"
 'John Friedrich,Adrian Zmed,Ernest Harden Jr.,Lewis Smith,Rachel Ward,Daryl Hannah,Akosua Busia,Joe Pantoliano,Mark Metcalf,Cindy Harrell,Irene Sanders,Richard Jacobs,Donna Pinder,Jim Youngs,Lori Butler,Anthony Maccario'
 'Dominique Swain,Brad Renfro,Bijou Phillips,Mischa Barton,Alberta Watson,Myles Jeffrey,Scott Thompson,Michael Murphy,Nora Zehetner,Jacob Pitts,Chelse Swain,Lacey Chabert,Melanie Griffith,Shawn Lawrence,Peter Snider,Sherry Miller,Marcia Bennett,Mairon Bennett,Liz Gordon,Tannis Burnett,Miranda Reeves,Anne Wessels,Keleigh Ardell,Howard Jerome,Bill Lake,Nora McLellan,Rafael Petardi,Patrick Patterson,David MacKay,Andrew Dolha,J.C. Kenny,Conrad Bergschneider,Damon Redfern'] etc. 

 
      The Mixed Cast with Highest Movies Count have '5' Movies Available is 'Dennis Quaid,Greg Kinnear,Common,Charlie Saxton,Will Sasso,Odessa Rae,Seth MacFarlane,Mike Meldman,Hugh Jackman,Kate Winslet,Julie Claire,Katie Finneran,Roy Jenkins,Rocky Russo,Anna Madigan,Jeremy Allen White,Liev Schreiber,Naomi Watts,Alex Cranmer,Julie Ann Emery,Austin Cope,Anna Faris,Chris Pratt,J.B. Smoove,Jarrad Paul,Maria Arcé,Aaron LaPlante,Kieran Culkin,Emma Stone,Arthur French,Josh Shuman,Cathy Cliften,Cherina Monteniques Scott,Richard Gere,Kate Bosworth,Jack McBrayer,Aasif Mandvi,Zach Lasry,Darby Lynn Totten,Marc Ambrose,Justin Long,Jason Sudeikis,Uma Thurman,Bobby Cannavale,Kristen Bell,John Hodgman,Leslie Bibb,Will Carlough,Katrina Bowden,Phil Crowley,Ed Blythe,Olivia Roberts Payne,Monique Delee,Julie McNiven,Jaron Henrie-McCrea,Sebastian Banes,Severyn Banes,Rightor Doyle,Raven Burnett,Montana Byrne,Jarrett Carter,Phillip Michaels,Christopher Mintz-Plasse,Chloë Grace Moretz,Patrick Warburton,Jimmy Bennett,Matt Walsh,Coleen Hoffman,Elbe Van Der Merwe,Gerard Butler,Seann William Scott,Johnny Knoxville,Esti Ginzburg,Halle Berry,Stephen Merchant,Sayed Badreya,Nicole Polizzi,Caryl West,Ricki Lander,Paloma Felisberto Bilson,Jasper Grey,Benny Harris,Zen Gesner,Terrence Howard,Aaron Jennings,Jared Dudley,Corey Brewer,Larry Eugene Sanders II,Jay Ellis,Brian Flaccus,Brett Davern,Evan Dumouchel,Sean A. Rosales,Logan Holladay,Mandy Kowalski,Eric Stuart,Elizabeth Banks,Josh Duhamel,Emily Alyn Lind,Michelle Gunn,Christina Linhardt,Greg Oliver Bodine,Adam Cagley,Liz Carey,Bronwyn Cornelius,Devin Eash,Ted Ely,Peter Farrelly,Jackie Flynn,Nate Hartley,James Hsu,Sara Kessler,Christopher Kirby,Martin Klebba,Kurt Leitner,Beth Littleford,Michael Marrio,Rob Moran,Eddie Napolillo,Vincent Ortega,George Paez,Victor Pagan,Martin Pfefferkorn,Tony Shalhoub,Davi Shane,Nicole Joy Tan,Charles B. Wessler,Mark L. Young', &

      The Mixed Cast with Lowest Movies Count have '1' Movies Available is 'Ryan Barton-Grimley,Jeremy Clark,Jes Mercer,Jonathen Wikholm,Antonio Brunetti,John Douglas,John S. Boles,Matthew Brumlow,Randall Cropp,Matthew Crow,Ryan Michael Decker,Malinda DeRouen,James Errico,Danny Fairchild,Britni Farber,Teresa Green,Jeff Hefel,Rory Jobst,Lori Keifer,Peter Kremidas,Sarah Lecander,Kevin Mannis,Burr Nissen,Heather Osthoff,Patrick Pankhurst,Carrie Pozdol,Christopher Purdy,Art Roche,Paige Sawin,Ari Schneider,Michael Smith,Mike Smith,Cora Vander Broek,Colleen Walker,Caryl West'
In [90]:
fig = px.pie(mixed_casts_data_movies[:10], names = 'Mixed Cast', values = 'Movies Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'Movies Count based on Mixed Cast')
fig.show()
In [91]:
# netflix_mixed_casts_movies = mixed_casts_data_movies[mixed_casts_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_casts_movies = netflix_mixed_casts_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
netflix_mixed_casts_high_movies = df_mixed_casts_high_movies.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_casts_high_movies = netflix_mixed_casts_high_movies.drop(['index'], axis = 1)
 
netflix_mixed_casts_low_movies = df_mixed_casts_high_movies.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_casts_low_movies = netflix_mixed_casts_low_movies.drop(['index'], axis = 1)
 
netflix_mixed_casts_high_movies.head(5)
Out[91]:
Mixed Cast Movies Count Netflix Hulu Prime Video Disney+
0 Deepak Chachra,Smita Malhotra,Tarun Ratnani 3 3 0 0 0
1 Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... 5 2 0 4 0
2 Christian Bale,Dominic Purcell,Sean Bean,Chris... 2 2 0 0 0
3 Ice Cube,Nia Long,John C. McGinley,Aleisha All... 2 2 0 1 0
4 Milla Jovovich,Oded Fehr,Ali Larter,Iain Glen,... 2 2 0 0 0
In [92]:
# hulu_mixed_casts_movies = mixed_casts_data_movies[mixed_casts_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_casts_movies = hulu_mixed_casts_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)
 
hulu_mixed_casts_high_movies = df_mixed_casts_high_movies.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_casts_high_movies = hulu_mixed_casts_high_movies.drop(['index'], axis = 1)
 
hulu_mixed_casts_low_movies = df_mixed_casts_high_movies.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_casts_low_movies = hulu_mixed_casts_low_movies.drop(['index'], axis = 1)
 
hulu_mixed_casts_high_movies.head(5)
Out[92]:
Mixed Cast Movies Count Netflix Hulu Prime Video Disney+
0 Asa Butterfield,Sophie Turner,Skyler Gisondo,W... 2 0 2 0 0
1 Jeannie Elias,Kat Feller,Sandy Fox,Cindy Robin... 2 2 2 0 0
2 Penélope Cruz,Carmen Maura,Lola Dueñas,Blanca ... 2 0 2 0 0
3 Katie Holmes,Benjamin Bratt,Charlie Hunnam,Zoo... 2 0 2 0 0
4 Robert De Niro,Ray Liotta,Joe Pesci,Lorraine B... 2 0 2 0 0
In [93]:
# prime_video_mixed_casts_movies = mixed_casts_data_movies[mixed_casts_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_casts_movies = prime_video_mixed_casts_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)
 
prime_video_mixed_casts_high_movies = df_mixed_casts_high_movies.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_casts_high_movies = prime_video_mixed_casts_high_movies.drop(['index'], axis = 1)
 
prime_video_mixed_casts_low_movies = df_mixed_casts_high_movies.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_casts_low_movies = prime_video_mixed_casts_low_movies.drop(['index'], axis = 1)
 
prime_video_mixed_casts_high_movies.head(5)
Out[93]:
Mixed Cast Movies Count Netflix Hulu Prime Video Disney+
0 Dennis Quaid,Greg Kinnear,Common,Charlie Saxto... 5 2 0 4 0
1 Shauna Macdonald,Natalie Mendoza,Alex Reid,Sas... 4 0 1 4 0
2 John Friedrich,Adrian Zmed,Ernest Harden Jr.,L... 3 0 0 3 0
3 Dominique Swain,Brad Renfro,Bijou Phillips,Mis... 3 0 0 3 0
4 Yuliya Peresild,Evgeniy Tsyganov,Oleg Vasilkov... 3 0 0 3 0
In [94]:
# disney_mixed_casts_movies = mixed_casts_data_movies[mixed_casts_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_casts_movies = disney_mixed_casts_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
 
disney_mixed_casts_high_movies = df_mixed_casts_high_movies.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_casts_high_movies = disney_mixed_casts_high_movies.drop(['index'], axis = 1)
 
disney_mixed_casts_low_movies = df_mixed_casts_high_movies.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_casts_low_movies = disney_mixed_casts_low_movies.drop(['index'], axis = 1)
 
disney_mixed_casts_high_movies.head(5)
Out[94]:
Mixed Cast Movies Count Netflix Hulu Prime Video Disney+
0 Hayley Mills,Dean Jones,Dorothy Provine,Roddy ... 2 0 0 0 2
1 Jeff Bridges,Garrett Hedlund,Olivia Wilde,Bruc... 2 0 0 0 2
2 Winston Hibler,Mogens Lind 2 0 0 0 2
3 Joshua Bassett,Corbin Bleu,Matt Cornett,Tim Fe... 2 0 0 0 2
4 Joaquin Phoenix,Jeremy Suarez,Jason Raize,Rick... 2 0 0 1 2
In [95]:
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_casts_data_movies['Movies Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_casts_data_movies['Movies Count'], ax = ax[1])
plt.show()
In [96]:
# Creating distinct dataframes only with the movies present on individual streaming platforms
netflix_mixed_casts_movies = mixed_casts_data_movies[mixed_casts_data_movies['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_casts_movies = netflix_mixed_casts_movies.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

hulu_mixed_casts_movies = mixed_casts_data_movies[mixed_casts_data_movies['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_casts_movies = hulu_mixed_casts_movies.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'Movies Count'], axis = 1)

prime_video_mixed_casts_movies = mixed_casts_data_movies[mixed_casts_data_movies['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_casts_movies = prime_video_mixed_casts_movies.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'Movies Count'], axis = 1)

disney_mixed_casts_movies = mixed_casts_data_movies[mixed_casts_data_movies['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_casts_movies = disney_mixed_casts_movies.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'Movies Count'], axis = 1)
In [97]:
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Cast Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(prime_video_mixed_casts_movies['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_casts_movies['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_casts_movies['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_casts_movies['Disney+'][:100], color = 'darkblue', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
In [98]:
print(f'''
      The Mixed Cast with Highest Movies Count Ever Got is '{df_mixed_casts_high_movies['Mixed Cast'][0]}' : '{df_mixed_casts_high_movies['Movies Count'].max()}'\n
      The Mixed Cast with Lowest Movies Count Ever Got is '{df_mixed_casts_low_movies['Mixed Cast'][0]}' : '{df_mixed_casts_low_movies['Movies Count'].min()}'\n
      
      The Mixed Cast with Highest Movies Count on 'Netflix' is '{netflix_mixed_casts_high_movies['Mixed Cast'][0]}' : '{netflix_mixed_casts_high_movies['Netflix'].max()}'\n
      The Mixed Cast with Lowest Movies Count on 'Netflix' is '{netflix_mixed_casts_low_movies['Mixed Cast'][0]}' : '{netflix_mixed_casts_low_movies['Netflix'].min()}'\n
      
      The Mixed Cast with Highest Movies Count on 'Hulu' is '{hulu_mixed_casts_high_movies['Mixed Cast'][0]}' : '{hulu_mixed_casts_high_movies['Hulu'].max()}'\n
      The Mixed Cast with Lowest Movies Count on 'Hulu' is '{hulu_mixed_casts_low_movies['Mixed Cast'][0]}' : '{hulu_mixed_casts_low_movies['Hulu'].min()}'\n
      
      The Mixed Cast with Highest Movies Count on 'Prime Video' is '{prime_video_mixed_casts_high_movies['Mixed Cast'][0]}' : '{prime_video_mixed_casts_high_movies['Prime Video'].max()}'\n
      The Mixed Cast with Lowest Movies Count on 'Prime Video' is '{prime_video_mixed_casts_low_movies['Mixed Cast'][0]}' : '{prime_video_mixed_casts_low_movies['Prime Video'].min()}'\n
      
      The Mixed Cast with Highest Movies Count on 'Disney+' is '{disney_mixed_casts_high_movies['Mixed Cast'][0]}' : '{disney_mixed_casts_high_movies['Disney+'].max()}'\n
      The Mixed Cast with Lowest Movies Count on 'Disney+' is '{disney_mixed_casts_low_movies['Mixed Cast'][0]}' : '{disney_mixed_casts_low_movies['Disney+'].min()}'\n 
      ''')

      The Mixed Cast with Highest Movies Count Ever Got is 'Dennis Quaid,Greg Kinnear,Common,Charlie Saxton,Will Sasso,Odessa Rae,Seth MacFarlane,Mike Meldman,Hugh Jackman,Kate Winslet,Julie Claire,Katie Finneran,Roy Jenkins,Rocky Russo,Anna Madigan,Jeremy Allen White,Liev Schreiber,Naomi Watts,Alex Cranmer,Julie Ann Emery,Austin Cope,Anna Faris,Chris Pratt,J.B. Smoove,Jarrad Paul,Maria Arcé,Aaron LaPlante,Kieran Culkin,Emma Stone,Arthur French,Josh Shuman,Cathy Cliften,Cherina Monteniques Scott,Richard Gere,Kate Bosworth,Jack McBrayer,Aasif Mandvi,Zach Lasry,Darby Lynn Totten,Marc Ambrose,Justin Long,Jason Sudeikis,Uma Thurman,Bobby Cannavale,Kristen Bell,John Hodgman,Leslie Bibb,Will Carlough,Katrina Bowden,Phil Crowley,Ed Blythe,Olivia Roberts Payne,Monique Delee,Julie McNiven,Jaron Henrie-McCrea,Sebastian Banes,Severyn Banes,Rightor Doyle,Raven Burnett,Montana Byrne,Jarrett Carter,Phillip Michaels,Christopher Mintz-Plasse,Chloë Grace Moretz,Patrick Warburton,Jimmy Bennett,Matt Walsh,Coleen Hoffman,Elbe Van Der Merwe,Gerard Butler,Seann William Scott,Johnny Knoxville,Esti Ginzburg,Halle Berry,Stephen Merchant,Sayed Badreya,Nicole Polizzi,Caryl West,Ricki Lander,Paloma Felisberto Bilson,Jasper Grey,Benny Harris,Zen Gesner,Terrence Howard,Aaron Jennings,Jared Dudley,Corey Brewer,Larry Eugene Sanders II,Jay Ellis,Brian Flaccus,Brett Davern,Evan Dumouchel,Sean A. Rosales,Logan Holladay,Mandy Kowalski,Eric Stuart,Elizabeth Banks,Josh Duhamel,Emily Alyn Lind,Michelle Gunn,Christina Linhardt,Greg Oliver Bodine,Adam Cagley,Liz Carey,Bronwyn Cornelius,Devin Eash,Ted Ely,Peter Farrelly,Jackie Flynn,Nate Hartley,James Hsu,Sara Kessler,Christopher Kirby,Martin Klebba,Kurt Leitner,Beth Littleford,Michael Marrio,Rob Moran,Eddie Napolillo,Vincent Ortega,George Paez,Victor Pagan,Martin Pfefferkorn,Tony Shalhoub,Davi Shane,Nicole Joy Tan,Charles B. Wessler,Mark L. Young' : '5'

      The Mixed Cast with Lowest Movies Count Ever Got is 'Ryan Barton-Grimley,Jeremy Clark,Jes Mercer,Jonathen Wikholm,Antonio Brunetti,John Douglas,John S. Boles,Matthew Brumlow,Randall Cropp,Matthew Crow,Ryan Michael Decker,Malinda DeRouen,James Errico,Danny Fairchild,Britni Farber,Teresa Green,Jeff Hefel,Rory Jobst,Lori Keifer,Peter Kremidas,Sarah Lecander,Kevin Mannis,Burr Nissen,Heather Osthoff,Patrick Pankhurst,Carrie Pozdol,Christopher Purdy,Art Roche,Paige Sawin,Ari Schneider,Michael Smith,Mike Smith,Cora Vander Broek,Colleen Walker,Caryl West' : '1'

      
      The Mixed Cast with Highest Movies Count on 'Netflix' is 'Deepak Chachra,Smita Malhotra,Tarun Ratnani' : '3'

      The Mixed Cast with Lowest Movies Count on 'Netflix' is 'Jose Anderson,Kimberle Crenshaw,Mary Easter,Larry Gibson,Rawn James,Vernon Jordan,Elena Kagan,Thurgood Marshall,John Paul Stevens,Roger Wilkins,Juan Williams' : '0'

      
      The Mixed Cast with Highest Movies Count on 'Hulu' is 'Asa Butterfield,Sophie Turner,Skyler Gisondo,Will Peltz,Aubrey Reynolds,Jillian Joy,Joseph Park,Caden J. Gregoire,Cassie Williams,Coral Chambers,Joey Miyashima,Rebecca Flinn-White,Mark Blockovich,Mary Elizabeth Boylan,Kami Christiansen,Nicole Duke,Christopher Escalante,Gerry Garcia,Cameron Harford,Ariel Jean,Andrew L. McClain,Jose Abraham Robledo,Hero J. Thomas' : '2'

      The Mixed Cast with Lowest Movies Count on 'Hulu' is 'Dennis Quaid,Greg Kinnear,Common,Charlie Saxton,Will Sasso,Odessa Rae,Seth MacFarlane,Mike Meldman,Hugh Jackman,Kate Winslet,Julie Claire,Katie Finneran,Roy Jenkins,Rocky Russo,Anna Madigan,Jeremy Allen White,Liev Schreiber,Naomi Watts,Alex Cranmer,Julie Ann Emery,Austin Cope,Anna Faris,Chris Pratt,J.B. Smoove,Jarrad Paul,Maria Arcé,Aaron LaPlante,Kieran Culkin,Emma Stone,Arthur French,Josh Shuman,Cathy Cliften,Cherina Monteniques Scott,Richard Gere,Kate Bosworth,Jack McBrayer,Aasif Mandvi,Zach Lasry,Darby Lynn Totten,Marc Ambrose,Justin Long,Jason Sudeikis,Uma Thurman,Bobby Cannavale,Kristen Bell,John Hodgman,Leslie Bibb,Will Carlough,Katrina Bowden,Phil Crowley,Ed Blythe,Olivia Roberts Payne,Monique Delee,Julie McNiven,Jaron Henrie-McCrea,Sebastian Banes,Severyn Banes,Rightor Doyle,Raven Burnett,Montana Byrne,Jarrett Carter,Phillip Michaels,Christopher Mintz-Plasse,Chloë Grace Moretz,Patrick Warburton,Jimmy Bennett,Matt Walsh,Coleen Hoffman,Elbe Van Der Merwe,Gerard Butler,Seann William Scott,Johnny Knoxville,Esti Ginzburg,Halle Berry,Stephen Merchant,Sayed Badreya,Nicole Polizzi,Caryl West,Ricki Lander,Paloma Felisberto Bilson,Jasper Grey,Benny Harris,Zen Gesner,Terrence Howard,Aaron Jennings,Jared Dudley,Corey Brewer,Larry Eugene Sanders II,Jay Ellis,Brian Flaccus,Brett Davern,Evan Dumouchel,Sean A. Rosales,Logan Holladay,Mandy Kowalski,Eric Stuart,Elizabeth Banks,Josh Duhamel,Emily Alyn Lind,Michelle Gunn,Christina Linhardt,Greg Oliver Bodine,Adam Cagley,Liz Carey,Bronwyn Cornelius,Devin Eash,Ted Ely,Peter Farrelly,Jackie Flynn,Nate Hartley,James Hsu,Sara Kessler,Christopher Kirby,Martin Klebba,Kurt Leitner,Beth Littleford,Michael Marrio,Rob Moran,Eddie Napolillo,Vincent Ortega,George Paez,Victor Pagan,Martin Pfefferkorn,Tony Shalhoub,Davi Shane,Nicole Joy Tan,Charles B. Wessler,Mark L. Young' : '0'

      
      The Mixed Cast with Highest Movies Count on 'Prime Video' is 'Dennis Quaid,Greg Kinnear,Common,Charlie Saxton,Will Sasso,Odessa Rae,Seth MacFarlane,Mike Meldman,Hugh Jackman,Kate Winslet,Julie Claire,Katie Finneran,Roy Jenkins,Rocky Russo,Anna Madigan,Jeremy Allen White,Liev Schreiber,Naomi Watts,Alex Cranmer,Julie Ann Emery,Austin Cope,Anna Faris,Chris Pratt,J.B. Smoove,Jarrad Paul,Maria Arcé,Aaron LaPlante,Kieran Culkin,Emma Stone,Arthur French,Josh Shuman,Cathy Cliften,Cherina Monteniques Scott,Richard Gere,Kate Bosworth,Jack McBrayer,Aasif Mandvi,Zach Lasry,Darby Lynn Totten,Marc Ambrose,Justin Long,Jason Sudeikis,Uma Thurman,Bobby Cannavale,Kristen Bell,John Hodgman,Leslie Bibb,Will Carlough,Katrina Bowden,Phil Crowley,Ed Blythe,Olivia Roberts Payne,Monique Delee,Julie McNiven,Jaron Henrie-McCrea,Sebastian Banes,Severyn Banes,Rightor Doyle,Raven Burnett,Montana Byrne,Jarrett Carter,Phillip Michaels,Christopher Mintz-Plasse,Chloë Grace Moretz,Patrick Warburton,Jimmy Bennett,Matt Walsh,Coleen Hoffman,Elbe Van Der Merwe,Gerard Butler,Seann William Scott,Johnny Knoxville,Esti Ginzburg,Halle Berry,Stephen Merchant,Sayed Badreya,Nicole Polizzi,Caryl West,Ricki Lander,Paloma Felisberto Bilson,Jasper Grey,Benny Harris,Zen Gesner,Terrence Howard,Aaron Jennings,Jared Dudley,Corey Brewer,Larry Eugene Sanders II,Jay Ellis,Brian Flaccus,Brett Davern,Evan Dumouchel,Sean A. Rosales,Logan Holladay,Mandy Kowalski,Eric Stuart,Elizabeth Banks,Josh Duhamel,Emily Alyn Lind,Michelle Gunn,Christina Linhardt,Greg Oliver Bodine,Adam Cagley,Liz Carey,Bronwyn Cornelius,Devin Eash,Ted Ely,Peter Farrelly,Jackie Flynn,Nate Hartley,James Hsu,Sara Kessler,Christopher Kirby,Martin Klebba,Kurt Leitner,Beth Littleford,Michael Marrio,Rob Moran,Eddie Napolillo,Vincent Ortega,George Paez,Victor Pagan,Martin Pfefferkorn,Tony Shalhoub,Davi Shane,Nicole Joy Tan,Charles B. Wessler,Mark L. Young' : '4'

      The Mixed Cast with Lowest Movies Count on 'Prime Video' is 'Ümit Bülent Dinçer,Nil Günal,Pelin Acar,Damla Aydinli,Ömer Duran,Sultan Köroglu Kiliç,Murat Sevis,Özbek Yildiz' : '0'

      
      The Mixed Cast with Highest Movies Count on 'Disney+' is 'Hayley Mills,Dean Jones,Dorothy Provine,Roddy McDowall,Neville Brand,Elsa Lanchester,William Demarest,Frank Gorshin,Richard Eastham,Grayson Hall,Tom Lowell,Richard Deacon,Iris Adrian,Liam Sullivan,Don Dorrell,Gene Blakely,Karl Held,Ed Wynn,Larry J. Blake,Syn Cat,John Cliff,Ben Lessy' : '2'

      The Mixed Cast with Lowest Movies Count on 'Disney+' is 'Dennis Quaid,Greg Kinnear,Common,Charlie Saxton,Will Sasso,Odessa Rae,Seth MacFarlane,Mike Meldman,Hugh Jackman,Kate Winslet,Julie Claire,Katie Finneran,Roy Jenkins,Rocky Russo,Anna Madigan,Jeremy Allen White,Liev Schreiber,Naomi Watts,Alex Cranmer,Julie Ann Emery,Austin Cope,Anna Faris,Chris Pratt,J.B. Smoove,Jarrad Paul,Maria Arcé,Aaron LaPlante,Kieran Culkin,Emma Stone,Arthur French,Josh Shuman,Cathy Cliften,Cherina Monteniques Scott,Richard Gere,Kate Bosworth,Jack McBrayer,Aasif Mandvi,Zach Lasry,Darby Lynn Totten,Marc Ambrose,Justin Long,Jason Sudeikis,Uma Thurman,Bobby Cannavale,Kristen Bell,John Hodgman,Leslie Bibb,Will Carlough,Katrina Bowden,Phil Crowley,Ed Blythe,Olivia Roberts Payne,Monique Delee,Julie McNiven,Jaron Henrie-McCrea,Sebastian Banes,Severyn Banes,Rightor Doyle,Raven Burnett,Montana Byrne,Jarrett Carter,Phillip Michaels,Christopher Mintz-Plasse,Chloë Grace Moretz,Patrick Warburton,Jimmy Bennett,Matt Walsh,Coleen Hoffman,Elbe Van Der Merwe,Gerard Butler,Seann William Scott,Johnny Knoxville,Esti Ginzburg,Halle Berry,Stephen Merchant,Sayed Badreya,Nicole Polizzi,Caryl West,Ricki Lander,Paloma Felisberto Bilson,Jasper Grey,Benny Harris,Zen Gesner,Terrence Howard,Aaron Jennings,Jared Dudley,Corey Brewer,Larry Eugene Sanders II,Jay Ellis,Brian Flaccus,Brett Davern,Evan Dumouchel,Sean A. Rosales,Logan Holladay,Mandy Kowalski,Eric Stuart,Elizabeth Banks,Josh Duhamel,Emily Alyn Lind,Michelle Gunn,Christina Linhardt,Greg Oliver Bodine,Adam Cagley,Liz Carey,Bronwyn Cornelius,Devin Eash,Ted Ely,Peter Farrelly,Jackie Flynn,Nate Hartley,James Hsu,Sara Kessler,Christopher Kirby,Martin Klebba,Kurt Leitner,Beth Littleford,Michael Marrio,Rob Moran,Eddie Napolillo,Vincent Ortega,George Paez,Victor Pagan,Martin Pfefferkorn,Tony Shalhoub,Davi Shane,Nicole Joy Tan,Charles B. Wessler,Mark L. Young' : '0'
In [99]:
print(f'''
      Accross All Platforms the Average Movies Count of Mixed Cast is '{round(mixed_casts_data_movies['Movies Count'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Cast on 'Netflix' is '{round(netflix_mixed_casts_movies['Netflix'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Cast on 'Hulu' is '{round(hulu_mixed_casts_movies['Hulu'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Cast on 'Prime Video' is '{round(prime_video_mixed_casts_movies['Prime Video'].mean(), ndigits = 2)}'\n
      The Average Movies Count of Mixed Cast on 'Disney+' is '{round(disney_mixed_casts_movies['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Movies Count of Mixed Cast is '1.02'

      The Average Movies Count of Mixed Cast on 'Netflix' is '1.01'

      The Average Movies Count of Mixed Cast on 'Hulu' is '1.01'

      The Average Movies Count of Mixed Cast on 'Prime Video' is '1.01'

      The Average Movies Count of Mixed Cast on 'Disney+' is '1.01'
In [100]:
print(f'''
      Accross All Platforms Total Count of Mixed Cast is '{mixed_casts_data_movies['Mixed Cast'].unique().shape[0]}'\n
      Total Count of Mixed Cast on 'Netflix' is '{netflix_mixed_casts_movies['Mixed Cast'].unique().shape[0]}'\n
      Total Count of Mixed Cast on 'Hulu' is '{hulu_mixed_casts_movies['Mixed Cast'].unique().shape[0]}'\n
      Total Count of Mixed Cast on 'Prime Video' is '{prime_video_mixed_casts_movies['Mixed Cast'].unique().shape[0]}'\n
      Total Count of Mixed Cast on 'Disney+' is '{disney_mixed_casts_movies['Mixed Cast'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Mixed Cast is '15354'

      Total Count of Mixed Cast on 'Netflix' is '3229'

      Total Count of Mixed Cast on 'Hulu' is '1024'

      Total Count of Mixed Cast on 'Prime Video' is '11280'

      Total Count of Mixed Cast on 'Disney+' is '528'
In [101]:
plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_casts_data_movies['Mixed Cast'][:5], y = mixed_casts_data_movies['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_casts_data_movies['Mixed Cast'][:5], y = mixed_casts_data_movies['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_casts_data_movies['Mixed Cast'][:5], y = mixed_casts_data_movies['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_casts_data_movies['Mixed Cast'][:5], y = mixed_casts_data_movies['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Cast', fontsize = 15)
plt.ylabel('Movies Count', fontsize = 15)
plt.show()
In [102]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_c_ax1 = sns.barplot(x = mixed_casts_data_movies['Mixed Cast'][:10], y = mixed_casts_data_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_c_ax2 = sns.barplot(x = mixed_casts_data_movies['Mixed Cast'][:10], y = mixed_casts_data_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_c_ax3 = sns.barplot(x = mixed_casts_data_movies['Mixed Cast'][:10], y = mixed_casts_data_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_c_ax4 = sns.barplot(x = mixed_casts_data_movies['Mixed Cast'][:10], y = mixed_casts_data_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_c_ax1.title.set_text(labels[0])
h_c_ax2.title.set_text(labels[1])
p_c_ax3.title.set_text(labels[2])
d_c_ax4.title.set_text(labels[3])
 
plt.show()
In [103]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_mc_ax1 = sns.lineplot(x = mixed_casts_data_movies['Mixed Cast'][:10], y = mixed_casts_data_movies['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_mc_ax2 = sns.lineplot(x = mixed_casts_data_movies['Mixed Cast'][:10], y = mixed_casts_data_movies['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_mc_ax3 = sns.lineplot(x = mixed_casts_data_movies['Mixed Cast'][:10], y = mixed_casts_data_movies['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_mc_ax4 = sns.lineplot(x = mixed_casts_data_movies['Mixed Cast'][:10], y = mixed_casts_data_movies['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_mc_ax1.title.set_text(labels[0])
h_mc_ax2.title.set_text(labels[1])
p_mc_ax3.title.set_text(labels[2])
d_mc_ax4.title.set_text(labels[3])
 
plt.show()
In [104]:
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Cast  Movies Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_casts_movies['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_casts_movies['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_casts_movies['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_casts_movies['Disney+'][:50], color = 'darkblue', legend = True)                                      

# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()
In [105]:
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_mc_ax1 = sns.barplot(x = netflix_mixed_casts_movies['Mixed Cast'][:10], y = netflix_mixed_casts_movies['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_mc_ax2 = sns.barplot(x = hulu_mixed_casts_movies['Mixed Cast'][:10], y = hulu_mixed_casts_movies['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_mc_ax3 = sns.barplot(x = prime_video_mixed_casts_movies['Mixed Cast'][:10], y = prime_video_mixed_casts_movies['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_mc_ax4 = sns.barplot(x = disney_mixed_casts_movies['Mixed Cast'][:10], y = disney_mixed_casts_movies['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_mc_ax1.title.set_text(labels[0])
h_mc_ax2.title.set_text(labels[1])
p_mc_ax3.title.set_text(labels[2])
d_mc_ax4.title.set_text(labels[3])
 
plt.show()
In [106]:
fig = go.Figure(go.Funnel(y = mixed_casts_data_movies['Mixed Cast'][:10], x = mixed_casts_data_movies['Movies Count'][:10]))
fig.show()